Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations891
Missing cells537
Missing cells (%)4.0%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory101.1 KiB
Average record size in memory116.1 B

Variable types

Numeric6
Categorical5
Unsupported4

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
Age is highly overall correlated with FamilySize and 5 other fieldsHigh correlation
FamilySize is highly overall correlated with Age and 5 other fieldsHigh correlation
Fare is highly overall correlated with Age and 2 other fieldsHigh correlation
Has_Cabin is highly overall correlated with Age and 6 other fieldsHigh correlation
Parch is highly overall correlated with Has_Cabin and 1 other fieldsHigh correlation
PassengerId is highly overall correlated with Age and 5 other fieldsHigh correlation
Pclass is highly overall correlated with Has_Cabin and 2 other fieldsHigh correlation
SibSp is highly overall correlated with isAloneHigh correlation
Survived is highly overall correlated with Age and 5 other fieldsHigh correlation
Title is highly overall correlated with isAloneHigh correlation
isAlone is highly overall correlated with Age and 8 other fieldsHigh correlation
Title has 537 (60.3%) missing valuesMissing
Name is an unsupported type, check if it needs cleaning or further analysisUnsupported
Sex is an unsupported type, check if it needs cleaning or further analysisUnsupported
Ticket is an unsupported type, check if it needs cleaning or further analysisUnsupported
Embarked is an unsupported type, check if it needs cleaning or further analysisUnsupported
SibSp has 71 (8.0%) zerosZeros
Parch has 141 (15.8%) zerosZeros

Reproduction

Analysis started2025-10-15 05:37:18.226862
Analysis finished2025-10-15 05:37:31.545081
Duration13.32 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

High correlation 

Distinct354
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.56902
Minimum1
Maximum889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-10-15T11:07:31.990580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q3312.5
95-th percentile765
Maximum889
Range888
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation265.13839
Coefficient of variation (CV)1.5544346
Kurtosis0.3729066
Mean170.56902
Median Absolute Deviation (MAD)0
Skewness1.3406645
Sum151977
Variance70298.367
MonotonicityNot monotonic
2025-10-15T11:07:32.903234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1538
60.4%
6461
 
0.1%
5881
 
0.1%
5861
 
0.1%
5821
 
0.1%
5811
 
0.1%
5791
 
0.1%
5781
 
0.1%
5721
 
0.1%
5681
 
0.1%
Other values (344)344
38.6%
ValueCountFrequency (%)
1538
60.4%
21
 
0.1%
41
 
0.1%
81
 
0.1%
91
 
0.1%
101
 
0.1%
111
 
0.1%
141
 
0.1%
171
 
0.1%
191
 
0.1%
ValueCountFrequency (%)
8891
0.1%
8861
0.1%
8811
0.1%
8801
0.1%
8751
0.1%
8721
0.1%
8701
0.1%
8671
0.1%
8641
0.1%
8621
0.1%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
1
716 
0
175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1716
80.4%
0175
 
19.6%

Length

2025-10-15T11:07:33.184652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T11:07:33.674206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1716
80.4%
0175
 
19.6%

Most occurring characters

ValueCountFrequency (%)
1716
80.4%
0175
 
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1716
80.4%
0175
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1716
80.4%
0175
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1716
80.4%
0175
 
19.6%

Pclass
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
1
644 
3
167 
2
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1644
72.3%
3167
 
18.7%
280
 
9.0%

Length

2025-10-15T11:07:34.261964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T11:07:34.477433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1644
72.3%
3167
 
18.7%
280
 
9.0%

Most occurring characters

ValueCountFrequency (%)
1644
72.3%
3167
 
18.7%
280
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1644
72.3%
3167
 
18.7%
280
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1644
72.3%
3167
 
18.7%
280
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1644
72.3%
3167
 
18.7%
280
 
9.0%

Name
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size7.1 KiB

Sex
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size7.1 KiB

Age
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.175275
Minimum0.42
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-10-15T11:07:34.827326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile1
Q11
median1
Q324
95-th percentile44
Maximum70
Range69.58
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.633329
Coefficient of variation (CV)1.3989212
Kurtosis0.64660117
Mean11.175275
Median Absolute Deviation (MAD)0
Skewness1.3274418
Sum9957.17
Variance244.40097
MonotonicityNot monotonic
2025-10-15T11:07:35.372908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1544
61.1%
2851
 
5.7%
2413
 
1.5%
1813
 
1.5%
2511
 
1.2%
210
 
1.1%
3610
 
1.1%
410
 
1.1%
299
 
1.0%
319
 
1.0%
Other values (60)211
 
23.7%
ValueCountFrequency (%)
0.421
 
0.1%
0.671
 
0.1%
0.752
 
0.2%
0.832
 
0.2%
0.921
 
0.1%
1544
61.1%
210
 
1.1%
36
 
0.7%
410
 
1.1%
53
 
0.3%
ValueCountFrequency (%)
701
 
0.1%
651
 
0.1%
641
 
0.1%
631
 
0.1%
603
0.3%
582
 
0.2%
561
 
0.1%
545
0.6%
531
 
0.1%
523
0.3%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1257015
Minimum0
Maximum8
Zeros71
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-10-15T11:07:35.589932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.90809186
Coefficient of variation (CV)0.80668978
Kurtosis27.456592
Mean1.1257015
Median Absolute Deviation (MAD)0
Skewness4.531717
Sum1003
Variance0.82463083
MonotonicityNot monotonic
2025-10-15T11:07:36.241107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1746
83.7%
071
 
8.0%
228
 
3.1%
418
 
2.0%
316
 
1.8%
87
 
0.8%
55
 
0.6%
ValueCountFrequency (%)
071
 
8.0%
1746
83.7%
228
 
3.1%
316
 
1.8%
418
 
2.0%
55
 
0.6%
87
 
0.8%
ValueCountFrequency (%)
87
 
0.8%
55
 
0.6%
418
 
2.0%
316
 
1.8%
228
 
3.1%
1746
83.7%
071
 
8.0%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98428732
Minimum0
Maximum6
Zeros141
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-10-15T11:07:36.564198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6549552
Coefficient of variation (CV)0.66541059
Kurtosis12.751808
Mean0.98428732
Median Absolute Deviation (MAD)0
Skewness2.2045182
Sum877
Variance0.42896632
MonotonicityNot monotonic
2025-10-15T11:07:36.774336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1655
73.5%
0141
 
15.8%
280
 
9.0%
55
 
0.6%
35
 
0.6%
44
 
0.4%
61
 
0.1%
ValueCountFrequency (%)
0141
 
15.8%
1655
73.5%
280
 
9.0%
35
 
0.6%
44
 
0.4%
55
 
0.6%
61
 
0.1%
ValueCountFrequency (%)
61
 
0.1%
55
 
0.6%
44
 
0.4%
35
 
0.6%
280
 
9.0%
1655
73.5%
0141
 
15.8%

Ticket
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size7.1 KiB

Fare
Real number (ℝ)

High correlation 

Distinct142
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.004069
Minimum1
Maximum512.3292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-10-15T11:07:37.105870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q323.45
95-th percentile89.1042
Maximum512.3292
Range511.3292
Interquartile range (IQR)22.45

Descriptive statistics

Standard deviation41.972976
Coefficient of variation (CV)2.0982219
Kurtosis31.886125
Mean20.004069
Median Absolute Deviation (MAD)0
Skewness4.5877342
Sum17823.625
Variance1761.7307
MonotonicityNot monotonic
2025-10-15T11:07:37.672619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1537
60.3%
2622
 
2.5%
14.45427
 
0.8%
31.2757
 
0.8%
16.17
 
0.8%
69.557
 
0.8%
15.57
 
0.8%
26.256
 
0.7%
24.156
 
0.7%
46.96
 
0.7%
Other values (132)279
31.3%
ValueCountFrequency (%)
1537
60.3%
6.49581
 
0.1%
7.04581
 
0.1%
7.05421
 
0.1%
7.22922
 
0.2%
7.251
 
0.1%
7.752
 
0.2%
7.7752
 
0.2%
7.85423
 
0.3%
7.9255
 
0.6%
ValueCountFrequency (%)
512.32921
 
0.1%
2634
0.4%
262.3752
0.2%
247.52082
0.2%
227.5251
 
0.1%
211.51
 
0.1%
211.33752
0.2%
164.86672
0.2%
153.46252
0.2%
151.553
0.3%

Embarked
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size7.1 KiB

Has_Cabin
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
1
647 
0
244 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1647
72.6%
0244
 
27.4%

Length

2025-10-15T11:07:38.042055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T11:07:38.225797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1647
72.6%
0244
 
27.4%

Most occurring characters

ValueCountFrequency (%)
1647
72.6%
0244
 
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1647
72.6%
0244
 
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1647
72.6%
0244
 
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1647
72.6%
0244
 
27.4%

FamilySize
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9046016
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-10-15T11:07:38.466639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6134585
Coefficient of variation (CV)0.84713704
Kurtosis9.159666
Mean1.9046016
Median Absolute Deviation (MAD)0
Skewness2.7274415
Sum1697
Variance2.6032485
MonotonicityNot monotonic
2025-10-15T11:07:38.628303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1537
60.3%
2161
 
18.1%
3102
 
11.4%
429
 
3.3%
622
 
2.5%
515
 
1.7%
712
 
1.3%
117
 
0.8%
86
 
0.7%
ValueCountFrequency (%)
1537
60.3%
2161
 
18.1%
3102
 
11.4%
429
 
3.3%
515
 
1.7%
622
 
2.5%
712
 
1.3%
86
 
0.7%
117
 
0.8%
ValueCountFrequency (%)
117
 
0.8%
86
 
0.7%
712
 
1.3%
622
 
2.5%
515
 
1.7%
429
 
3.3%
3102
 
11.4%
2161
 
18.1%
1537
60.3%

isAlone
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
1
537 
0
354 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Length

2025-10-15T11:07:38.795092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T11:07:38.988255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Most occurring characters

ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Title
Categorical

High correlation  Missing 

Distinct5
Distinct (%)1.4%
Missing537
Missing (%)60.3%
Memory size7.1 KiB
Mr
120 
Mrs
105 
Miss
82 
Master
40 
Rare
 
7

Length

Max length6
Median length4
Mean length3.2514124
Min length2

Characters and Unicode

Total characters1151
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMr
2nd rowMrs
3rd rowMrs
4th rowMaster
5th rowMrs

Common Values

ValueCountFrequency (%)
Mr120
 
13.5%
Mrs105
 
11.8%
Miss82
 
9.2%
Master40
 
4.5%
Rare7
 
0.8%
(Missing)537
60.3%

Length

2025-10-15T11:07:39.128556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T11:07:39.282160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mr120
33.9%
mrs105
29.7%
miss82
23.2%
master40
 
11.3%
rare7
 
2.0%

Most occurring characters

ValueCountFrequency (%)
M347
30.1%
s309
26.8%
r272
23.6%
i82
 
7.1%
a47
 
4.1%
e47
 
4.1%
t40
 
3.5%
R7
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M347
30.1%
s309
26.8%
r272
23.6%
i82
 
7.1%
a47
 
4.1%
e47
 
4.1%
t40
 
3.5%
R7
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M347
30.1%
s309
26.8%
r272
23.6%
i82
 
7.1%
a47
 
4.1%
e47
 
4.1%
t40
 
3.5%
R7
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M347
30.1%
s309
26.8%
r272
23.6%
i82
 
7.1%
a47
 
4.1%
e47
 
4.1%
t40
 
3.5%
R7
 
0.6%

Interactions

2025-10-15T11:07:28.770478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:20.259320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:22.618396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:23.989699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:25.636720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:27.000074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:29.065666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:20.900971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:22.889444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:24.244181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:25.875079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:27.396949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:29.558139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:21.159587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:23.122109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:24.502568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:26.122674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:27.904546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:29.767930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:21.770456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:23.360802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:24.906708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:26.338304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:28.109029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:29.988592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:22.108445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:23.596635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:25.125260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:26.584779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:28.344611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:30.298625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:22.384927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:23.780933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:25.350539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:26.839315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T11:07:28.538039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-15T11:07:39.446896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeFamilySizeFareHas_CabinParchPassengerIdPclassSibSpSurvivedTitleisAlone
Age1.0000.8440.8950.675-0.2300.8680.491-0.0470.5910.4110.883
FamilySize0.8441.0000.9330.5910.0670.9250.4950.1460.5110.1950.642
Fare0.8950.9331.0000.071-0.0940.9240.0690.0270.1140.0490.448
Has_Cabin0.6750.5910.0711.0000.5730.6640.9400.4600.6180.0790.753
Parch-0.2300.067-0.0940.5731.000-0.1170.392-0.0910.4900.2200.735
PassengerId0.8680.9250.9240.664-0.1171.0000.480-0.0070.5460.0550.901
Pclass0.4910.4950.0690.9400.3920.4801.0000.3870.6490.1720.762
SibSp-0.0470.1460.0270.460-0.091-0.0070.3871.0000.4380.2420.537
Survived0.5910.5110.1140.6180.4900.5460.6490.4381.0000.4970.605
Title0.4110.1950.0490.0790.2200.0550.1720.2420.4971.0001.000
isAlone0.8830.6420.4480.7530.7350.9010.7620.5370.6051.0001.000

Missing values

2025-10-15T11:07:30.721189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-15T11:07:31.293156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedHas_CabinFamilySizeisAloneTitle
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500S020Mr
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C120Mrs
2111111.01111.00001111NaN
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000S120Mrs
4111111.01111.00001111NaN
5111111.01111.00001111NaN
6111111.01111.00001111NaN
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750S050Master
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333S030Mrs
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708C020Mrs
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedHas_CabinFamilySizeisAloneTitle
881111111.01111.0001111NaN
882111111.01111.0001111NaN
883111111.01111.0001111NaN
884111111.01111.0001111NaN
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.125Q060Mrs
886111111.01111.0001111NaN
887111111.01111.0001111NaN
88888903Johnston, Miss. Catherine Helen "Carrie"female28.012W./C. 660723.450S040Miss
889111111.01111.0001111NaN
890111111.01111.0001111NaN

Duplicate rows

Most frequently occurring

PassengerIdSurvivedPclassAgeSibSpParchFareHas_CabinFamilySizeisAloneTitle# duplicates
01111.0111.0111NaN537